Objective: assess racial-ethnic mix in adolescent suicides and contrast with overall suicides.

Other research questions: evolution of the racial-ethnic distribution of adolescent suicides over time (+ relative to the proportion of the population in that age group)

In parallel: evolution of the racial-ethnic distribution of overall suicides and adult suicides (20+) over time (+ relative to the proportion of the US population -- overall or 20+)

For now, we can focus on the national level.

However, we could also assess whether differences among racial-ethnic subgroups are more pronounced in certain HHS regions and/or states.

Along these lines, health journalists at CNN, US News, and NBC were most interested in the racial-ethnic mix in the 5 states with a stat. sig. increase in the absolute number of suicides + proportion outcome as well as California (stat. sig. increase in the proportion outcome only).

Data extraction :

CDC Wonder, Provisional Mortality Statistics, 2018 through Last Month Request & Current Final Multiple Cause of Death Data

Groupby: Residence HHS Regions, Gender, Year, Single Race 6, Hispanic Origin

Cause of death: Intentional self-harm

7 files:

• All years "Overall" (age-adjusted)
• 10-19 years "10-19"
• 20-64 years "20-64"
• 25-64 years "25-64" (age-adjusted)
• 20+ years "20plus"
• 25+ years "25plus" (age-adjusted)

• 65+ years "65plus" (age-adjusted)

  Years after 2010

# Run this code to keep plotly figures when exporting the notebook in HTML
import plotly
plotly.offline.init_notebook_mode()

from wonder_utils import SuicideData

sd = SuicideData()  # or? reject_list=["More than one race", "Not Stated"]

def plot_func(color, by, age_cat, age_cat_name,
              slice, rows, legend_text, by_list,
              plot_age_adjusted, additional_filename_text,
              other_data_slice={}, additional_subplot_title=""):
    data_slice = {"age_strat": slice}
    for key, value in list(other_data_slice.items()):
        data_slice[key] = value
    plot_params = {"x": "year",
                   "color": color,
                   "by": by,
                   "scatter": False,
                   "rows": rows,
                   "data_slice": data_slice,
                   "second_y": {"secondary_y": True,
                                "y": "pop_share",
                                "line_param": {"dash": "dot"}}}
    if not(by_list is None):
        plot_params["by_list"] = by_list
    kwargs = {"secondary_range": [0, 100],
              "secondary_ticksuffix": "%",
              "hide_title": True,
              "second_y_title_text": "% of the population in this age group",
              "legend_text": legend_text,
              "additional_subplot_title": additional_subplot_title}

    plot_params["y"] = "deaths"
    kwargs["y_title_text"] = f"Absolute count of suicides among {age_cat_name} ({age_cat})"
    kwargs["plot_filename"] = f"{age_cat_name}_{age_cat}_{color}_{plot_params['y']}" + additional_filename_text
    plot_params.update(kwargs)

    sd.plot(**plot_params
        )

    plot_params["y"] = "suicide_proportion"
    kwargs["y_title_text"] = f"Proportion of suicides occurring among {age_cat_name} ({age_cat})"
    kwargs["plot_filename"] = f"{age_cat_name}_{age_cat}_{color}_{plot_params['y']}" + additional_filename_text
    kwargs["primary_ticksuffix"] = "%"
    plot_params.update(kwargs)

    sd.plot(**plot_params
        )

    plot_params["y"] = "suicide_proportion_2"
    kwargs["y_title_text"] = f"Proportion of suicides occurring among {age_cat_name} ({age_cat})"
    kwargs["plot_filename"] = f"{age_cat_name}_{age_cat}_{color}_{plot_params['y']}" + additional_filename_text
    plot_params.update(kwargs)

    sd.plot(**plot_params
        )

    kwargs.pop("primary_ticksuffix")
    plot_params.pop("primary_ticksuffix")

    plot_params["y"] = "suicide_per_100k"
    kwargs["y_title_text"] = f"Crude suicide rate among {age_cat_name} ({age_cat})"
    kwargs["plot_filename"] = f"{age_cat_name}_{age_cat}_{color}_{plot_params['y']}" + additional_filename_text
    plot_params.update(kwargs)

    sd.plot(**plot_params
        )

    if plot_age_adjusted:

        plot_params["y"] = "age_adjusted_rate"
        kwargs["y_title_text"] = f"Age-adjusted suicide rate among {age_cat_name} ({age_cat})"
        kwargs["plot_filename"] = f"working-age_{plot_params['color']}_{plot_params['y']}" + additional_filename_text
        plot_params.update(kwargs)

        sd.plot(**plot_params
            )

Analysis at the national level

Subpopulation: adolescents (10-19) - 16 graphs

By Gender

plot_func(color="gender", by="age_strat",
          age_cat="10-19",
          age_cat_name="adolescents",
          slice="10-19",
          rows=1,
          legend_text="Gender",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Ethnicity

plot_func(color="ethnicity", by="age_strat",
          age_cat="10-19",
          age_cat_name="adolescents",
          slice="10-19",
          rows=1,
          legend_text="Ethnicity",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Race

plot_func(color="race", by="age_strat",
          age_cat="10-19",
          age_cat_name="adolescents",
          slice="10-19",
          rows=1,
          legend_text="Race",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Race-Ethnicity

plot_func(color="ethno_race_4_cat", by="age_strat",
          age_cat="10-19",
          age_cat_name="adolescents",
          slice="10-19",
          rows=1,
          legend_text="Race-Ethnicity",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

Subpopulation: working-age adults (20-64) - 16 graphs

By Gender

plot_func(color="gender", by="age_strat",
          age_cat="20-64",
          age_cat_name="working-age adults",
          slice="20-64",
          rows=1,
          legend_text="Gender",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Ethnicity

plot_func(color="ethnicity", by="age_strat",
          age_cat="20-64",
          age_cat_name="working-age adults",
          slice="20-64",
          rows=1,
          legend_text="Ethnicity",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Race

plot_func(color="race", by="age_strat",
          age_cat="20-64",
          age_cat_name="working-age adults",
          slice="20-64",
          rows=1,
          legend_text="Race",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Race-Ethnicity

plot_func(color="ethno_race_4_cat", by="age_strat",
          age_cat="20-64",
          age_cat_name="working-age adults",
          slice="20-64",
          rows=1,
          legend_text="Race-Ethnicity",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

Subpopulation: older adults (65plus) - 16 graphs

By Gender

plot_func(color="gender", by="age_strat",
          age_cat="65plus",
          age_cat_name="older adults",
          slice="65plus",
          rows=1,
          legend_text="Gender",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Ethnicity

plot_func(color="ethnicity", by="age_strat",
          age_cat="65plus",
          age_cat_name="older adults",
          slice="65plus",
          rows=1,
          legend_text="Ethnicity",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Race

plot_func(color="race", by="age_strat",
          age_cat="65plus",
          age_cat_name="older adults",
          slice="65plus",
          rows=1,
          legend_text="Race",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Race-Ethnicity

plot_func(color="ethno_race_4_cat", by="age_strat",
          age_cat="65plus",
          age_cat_name="older adults",
          slice="65plus",
          rows=1,
          legend_text="Race-Ethnicity",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

Subpopulation: all adults (20plus) - 16 graphs

By Gender

plot_func(color="gender", by="age_strat",
          age_cat="20plus",
          age_cat_name="all adults",
          slice="20plus",
          rows=1,
          legend_text="Gender",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Ethnicity

plot_func(color="ethnicity", by="age_strat",
          age_cat="20plus",
          age_cat_name="all adults",
          slice="20plus",
          rows=1,
          legend_text="Ethnicity",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Race

plot_func(color="race", by="age_strat",
          age_cat="20plus",
          age_cat_name="all adults",
          slice="20plus",
          rows=1,
          legend_text="Race",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Race-Ethnicity

plot_func(color="ethno_race_4_cat", by="age_strat",
          age_cat="20plus",
          age_cat_name="all adults",
          slice="20plus",
          rows=1,
          legend_text="Race-Ethnicity",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

Subpopulation: everyone, all age groups (Overall) - 16 graphs

By Gender

plot_func(color="gender", by="age_strat",
          age_cat="Overall",
          age_cat_name="everyone, all age groups",
          slice="Overall",
          rows=1,
          legend_text="Gender",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Ethnicity

plot_func(color="ethnicity", by="age_strat",
          age_cat="Overall",
          age_cat_name="everyone, all age groups",
          slice="Overall",
          rows=1,
          legend_text="Ethnicity",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Race

plot_func(color="race", by="age_strat",
          age_cat="Overall",
          age_cat_name="everyone, all age groups",
          slice="Overall",
          rows=1,
          legend_text="Race",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

By Race-Ethnicity

plot_func(color="ethno_race_4_cat", by="age_strat",
          age_cat="Overall",
          age_cat_name="everyone, all age groups",
          slice="Overall",
          rows=1,
          legend_text="Race-Ethnicity",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="")

Analysis by HHS region

First type of visualizations: one subplot by HHS region

Subpopulation: adolescents (10-19) - 16 graphs, with 10 HHS-region subplots on each

By Gender

plot_func(color="gender", by="hhs",
          age_cat="10-19",
          age_cat_name="adolescents",
          slice="10-19",
          rows=2,
          legend_text="Gender",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Ethnicity

plot_func(color="ethnicity", by="hhs",
          age_cat="10-19",
          age_cat_name="adolescents",
          slice="10-19",
          rows=2,
          legend_text="Ethnicity",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Race

plot_func(color="race", by="hhs",
          age_cat="10-19",
          age_cat_name="adolescents",
          slice="10-19",
          rows=2,
          legend_text="Race",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Race-Ethnicity

plot_func(color="ethno_race_4_cat", by="hhs",
          age_cat="10-19",
          age_cat_name="adolescents",
          slice="10-19",
          rows=2,
          legend_text="Race-Ethnicity",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

Subpopulation: working-age adults (20-64) - 16 graphs, with 10 HHS-region subplots on each

By Gender

plot_func(color="gender", by="hhs",
          age_cat="20-64",
          age_cat_name="working-age adults",
          slice="20-64",
          rows=2,
          legend_text="Gender",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Ethnicity

plot_func(color="ethnicity", by="hhs",
          age_cat="20-64",
          age_cat_name="working-age adults",
          slice="20-64",
          rows=2,
          legend_text="Ethnicity",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Race

plot_func(color="race", by="hhs",
          age_cat="20-64",
          age_cat_name="working-age adults",
          slice="20-64",
          rows=2,
          legend_text="Race",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Race-Ethnicity

plot_func(color="ethno_race_4_cat", by="hhs",
          age_cat="20-64",
          age_cat_name="working-age adults",
          slice="20-64",
          rows=2,
          legend_text="Race-Ethnicity",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

Subpopulation: older adults (65plus) - 16 graphs, with 10 HHS-region subplots on each

By Gender

plot_func(color="gender", by="hhs",
          age_cat="65plus",
          age_cat_name="older adults",
          slice="65plus",
          rows=2,
          legend_text="Gender",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Ethnicity

plot_func(color="ethnicity", by="hhs",
          age_cat="65plus",
          age_cat_name="older adults",
          slice="65plus",
          rows=2,
          legend_text="Ethnicity",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Race

plot_func(color="race", by="hhs",
          age_cat="65plus",
          age_cat_name="older adults",
          slice="65plus",
          rows=2,
          legend_text="Race",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Race-Ethnicity

plot_func(color="ethno_race_4_cat", by="hhs",
          age_cat="65plus",
          age_cat_name="older adults",
          slice="65plus",
          rows=2,
          legend_text="Race-Ethnicity",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

Subpopulation: all adults (20plus) - 16 graphs, with 10 HHS-region subplots on each

By Gender

plot_func(color="gender", by="hhs",
          age_cat="20plus",
          age_cat_name="all adults",
          slice="20plus",
          rows=2,
          legend_text="Gender",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Ethnicity

plot_func(color="ethnicity", by="hhs",
          age_cat="20plus",
          age_cat_name="all adults",
          slice="20plus",
          rows=2,
          legend_text="Ethnicity",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Race

plot_func(color="race", by="hhs",
          age_cat="20plus",
          age_cat_name="all adults",
          slice="20plus",
          rows=2,
          legend_text="Race",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Race-Ethnicity

plot_func(color="ethno_race_4_cat", by="hhs",
          age_cat="20plus",
          age_cat_name="all adults",
          slice="20plus",
          rows=2,
          legend_text="Race-Ethnicity",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

Subpopulation: everyone, all age groups (Overall) - 16 graphs, with 10 HHS-region subplots on each

By Gender

plot_func(color="gender", by="hhs",
          age_cat="Overall",
          age_cat_name="everyone, all age groups",
          slice="Overall",
          rows=2,
          legend_text="Gender",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Ethnicity

plot_func(color="ethnicity", by="hhs",
          age_cat="Overall",
          age_cat_name="everyone, all age groups",
          slice="Overall",
          rows=2,
          legend_text="Ethnicity",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Race

plot_func(color="race", by="hhs",
          age_cat="Overall",
          age_cat_name="everyone, all age groups",
          slice="Overall",
          rows=2,
          legend_text="Race",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

By Race-Ethnicity

plot_func(color="ethno_race_4_cat", by="hhs",
          age_cat="Overall",
          age_cat_name="everyone, all age groups",
          slice="Overall",
          rows=2,
          legend_text="Race-Ethnicity",
          by_list=["HHS" + str(i) for i in range(1, 11)],
          plot_age_adjusted=False,
          additional_filename_text="_hhs_1")

Second type of visualizations: all HHS regions on the same plot

Subpopulation: adolescents (10-19) - 16 graphs

By Gender

plot_func(color="hhs", by="gender",
          age_cat="10-19",
          age_cat_name="adolescents",
          slice="10-19",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Ethnicity

plot_func(color="hhs", by="ethnicity",
          age_cat="10-19",
          age_cat_name="adolescents",
          slice="10-19",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Race

plot_func(color="hhs", by="race",
          age_cat="10-19",
          age_cat_name="adolescents",
          slice="10-19",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Race-Ethnicity

plot_func(color="hhs", by="ethno_race_4_cat",
          age_cat="10-19",
          age_cat_name="adolescents",
          slice="10-19",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

Subpopulation: working-age adults (20-64) - 16 graphs

By Gender

plot_func(color="hhs", by="gender",
          age_cat="20-64",
          age_cat_name="working-age adults",
          slice="20-64",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Ethnicity

plot_func(color="hhs", by="ethnicity",
          age_cat="20-64",
          age_cat_name="working-age adults",
          slice="20-64",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Race

plot_func(color="hhs", by="race",
          age_cat="20-64",
          age_cat_name="working-age adults",
          slice="20-64",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Race-Ethnicity

plot_func(color="hhs", by="ethno_race_4_cat",
          age_cat="20-64",
          age_cat_name="working-age adults",
          slice="20-64",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

Subpopulation: older adults (65plus) - 16 graphs

By Gender

plot_func(color="hhs", by="gender",
          age_cat="65plus",
          age_cat_name="older adults",
          slice="65plus",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Ethnicity

plot_func(color="hhs", by="ethnicity",
          age_cat="65plus",
          age_cat_name="older adults",
          slice="65plus",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Race

plot_func(color="hhs", by="race",
          age_cat="65plus",
          age_cat_name="older adults",
          slice="65plus",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Race-Ethnicity

plot_func(color="hhs", by="ethno_race_4_cat",
          age_cat="65plus",
          age_cat_name="older adults",
          slice="65plus",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

Subpopulation: all adults (20plus) - 16 graphs

By Gender

plot_func(color="hhs", by="gender",
          age_cat="20plus",
          age_cat_name="all adults",
          slice="20plus",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Ethnicity

plot_func(color="hhs", by="ethnicity",
          age_cat="20plus",
          age_cat_name="all adults",
          slice="20plus",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Race

plot_func(color="hhs", by="race",
          age_cat="20plus",
          age_cat_name="all adults",
          slice="20plus",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Race-Ethnicity

plot_func(color="hhs", by="ethno_race_4_cat",
          age_cat="20plus",
          age_cat_name="all adults",
          slice="20plus",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

Subpopulation: everyone, all age groups (Overall) - 16 graphs

By Gender

plot_func(color="hhs", by="gender",
          age_cat="Overall",
          age_cat_name="everyone, all age groups",
          slice="Overall",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Ethnicity

plot_func(color="hhs", by="ethnicity",
          age_cat="Overall",
          age_cat_name="everyone, all age groups",
          slice="Overall",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Race

plot_func(color="hhs", by="race",
          age_cat="Overall",
          age_cat_name="everyone, all age groups",
          slice="Overall",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

By Race-Ethnicity

plot_func(color="hhs", by="ethno_race_4_cat",
          age_cat="Overall",
          age_cat_name="everyone, all age groups",
          slice="Overall",
          rows=1,
          legend_text="HHS Region",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="_hhs_2")

Additional graphs

Adolescents females, by race

plot_func(color="race", by="age_strat",
          age_cat="10-19",
          age_cat_name="adolescents",
          slice="10-19",
          rows=1,
          legend_text="Race",
          by_list=None,
          plot_age_adjusted=False,
          additional_filename_text="",
          other_data_slice={"gender": "Female",
                            "race": ["Black", "White"]},
          additional_subplot_title=" - Gender=Female")